class KMeansModel extends Model[KMeansModel] with KMeansParams with GeneralMLWritable with HasTrainingSummary[KMeansSummary]
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- KMeansModel
- HasTrainingSummary
- GeneralMLWritable
- MLWritable
- KMeansParams
- HasMaxBlockSizeInMB
- HasSolver
- HasWeightCol
- HasDistanceMeasure
- HasTol
- HasPredictionCol
- HasSeed
- HasFeaturesCol
- HasMaxIter
- Model
- Transformer
- PipelineStage
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Parameters
A list of (hyper-)parameter keys this algorithm can take. Users can set and get the parameter values through setters and getters, respectively.
-
final
val
distanceMeasure: Param[String]
Param for The distance measure.
Param for The distance measure. Supported options: 'euclidean' and 'cosine'.
- Definition Classes
- HasDistanceMeasure
-
final
val
featuresCol: Param[String]
Param for features column name.
Param for features column name.
- Definition Classes
- HasFeaturesCol
-
final
val
k: IntParam
The number of clusters to create (k).
The number of clusters to create (k). Must be > 1. Note that it is possible for fewer than k clusters to be returned, for example, if there are fewer than k distinct points to cluster. Default: 2.
- Definition Classes
- KMeansParams
- Annotations
- @Since( "1.5.0" )
-
final
val
maxIter: IntParam
Param for maximum number of iterations (>= 0).
Param for maximum number of iterations (>= 0).
- Definition Classes
- HasMaxIter
-
final
val
predictionCol: Param[String]
Param for prediction column name.
Param for prediction column name.
- Definition Classes
- HasPredictionCol
-
final
val
seed: LongParam
Param for random seed.
Param for random seed.
- Definition Classes
- HasSeed
-
final
val
tol: DoubleParam
Param for the convergence tolerance for iterative algorithms (>= 0).
Param for the convergence tolerance for iterative algorithms (>= 0).
- Definition Classes
- HasTol
-
final
val
weightCol: Param[String]
Param for weight column name.
Param for weight column name. If this is not set or empty, we treat all instance weights as 1.0.
- Definition Classes
- HasWeightCol
Members
-
final
def
clear(param: Param[_]): KMeansModel.this.type
Clears the user-supplied value for the input param.
Clears the user-supplied value for the input param.
- Definition Classes
- Params
-
def
clusterCenters: Array[Vector]
- Annotations
- @Since( "2.0.0" )
-
def
copy(extra: ParamMap): KMeansModel
Creates a copy of this instance with the same UID and some extra params.
Creates a copy of this instance with the same UID and some extra params. Subclasses should implement this method and set the return type properly. See
defaultCopy()
.- Definition Classes
- KMeansModel → Model → Transformer → PipelineStage → Params
- Annotations
- @Since( "1.5.0" )
-
def
explainParam(param: Param[_]): String
Explains a param.
Explains a param.
- param
input param, must belong to this instance.
- returns
a string that contains the input param name, doc, and optionally its default value and the user-supplied value
- Definition Classes
- Params
-
def
explainParams(): String
Explains all params of this instance.
Explains all params of this instance. See
explainParam()
.- Definition Classes
- Params
-
final
def
extractParamMap(): ParamMap
extractParamMap
with no extra values.extractParamMap
with no extra values.- Definition Classes
- Params
-
final
def
extractParamMap(extra: ParamMap): ParamMap
Extracts the embedded default param values and user-supplied values, and then merges them with extra values from input into a flat param map, where the latter value is used if there exist conflicts, i.e., with ordering: default param values less than user-supplied values less than extra.
Extracts the embedded default param values and user-supplied values, and then merges them with extra values from input into a flat param map, where the latter value is used if there exist conflicts, i.e., with ordering: default param values less than user-supplied values less than extra.
- Definition Classes
- Params
-
final
def
get[T](param: Param[T]): Option[T]
Optionally returns the user-supplied value of a param.
Optionally returns the user-supplied value of a param.
- Definition Classes
- Params
-
final
def
getDefault[T](param: Param[T]): Option[T]
Gets the default value of a parameter.
Gets the default value of a parameter.
- Definition Classes
- Params
-
final
def
getOrDefault[T](param: Param[T]): T
Gets the value of a param in the embedded param map or its default value.
Gets the value of a param in the embedded param map or its default value. Throws an exception if neither is set.
- Definition Classes
- Params
-
def
getParam(paramName: String): Param[Any]
Gets a param by its name.
Gets a param by its name.
- Definition Classes
- Params
-
final
def
hasDefault[T](param: Param[T]): Boolean
Tests whether the input param has a default value set.
Tests whether the input param has a default value set.
- Definition Classes
- Params
-
def
hasParam(paramName: String): Boolean
Tests whether this instance contains a param with a given name.
Tests whether this instance contains a param with a given name.
- Definition Classes
- Params
-
def
hasParent: Boolean
Indicates whether this Model has a corresponding parent.
-
def
hasSummary: Boolean
Indicates whether a training summary exists for this model instance.
Indicates whether a training summary exists for this model instance.
- Definition Classes
- HasTrainingSummary
- Annotations
- @Since( "3.0.0" )
-
final
def
isDefined(param: Param[_]): Boolean
Checks whether a param is explicitly set or has a default value.
Checks whether a param is explicitly set or has a default value.
- Definition Classes
- Params
-
final
def
isSet(param: Param[_]): Boolean
Checks whether a param is explicitly set.
Checks whether a param is explicitly set.
- Definition Classes
- Params
-
lazy val
numFeatures: Int
- Annotations
- @Since( "3.0.0" )
-
lazy val
params: Array[Param[_]]
Returns all params sorted by their names.
Returns all params sorted by their names. The default implementation uses Java reflection to list all public methods that have no arguments and return Param.
- Definition Classes
- Params
- Note
Developer should not use this method in constructor because we cannot guarantee that this variable gets initialized before other params.
-
var
parent: Estimator[KMeansModel]
The parent estimator that produced this model.
The parent estimator that produced this model.
- Definition Classes
- Model
- Note
For ensembles' component Models, this value can be null.
-
def
predict(features: Vector): Int
- Annotations
- @Since( "3.0.0" )
-
def
save(path: String): Unit
Saves this ML instance to the input path, a shortcut of
write.save(path)
.Saves this ML instance to the input path, a shortcut of
write.save(path)
.- Definition Classes
- MLWritable
- Annotations
- @Since( "1.6.0" ) @throws( ... )
-
final
def
set[T](param: Param[T], value: T): KMeansModel.this.type
Sets a parameter in the embedded param map.
Sets a parameter in the embedded param map.
- Definition Classes
- Params
-
def
setParent(parent: Estimator[KMeansModel]): KMeansModel
Sets the parent of this model (Java API).
Sets the parent of this model (Java API).
- Definition Classes
- Model
-
def
summary: KMeansSummary
Gets summary of model on training set.
Gets summary of model on training set. An exception is thrown if
hasSummary
is false.- Definition Classes
- KMeansModel → HasTrainingSummary
- Annotations
- @Since( "2.0.0" )
-
def
toString(): String
- Definition Classes
- KMeansModel → Identifiable → AnyRef → Any
- Annotations
- @Since( "3.0.0" )
-
def
transform(dataset: Dataset[_]): DataFrame
Transforms the input dataset.
Transforms the input dataset.
- Definition Classes
- KMeansModel → Transformer
- Annotations
- @Since( "2.0.0" )
-
def
transform(dataset: Dataset[_], paramMap: ParamMap): DataFrame
Transforms the dataset with provided parameter map as additional parameters.
Transforms the dataset with provided parameter map as additional parameters.
- dataset
input dataset
- paramMap
additional parameters, overwrite embedded params
- returns
transformed dataset
- Definition Classes
- Transformer
- Annotations
- @Since( "2.0.0" )
-
def
transform(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): DataFrame
Transforms the dataset with optional parameters
Transforms the dataset with optional parameters
- dataset
input dataset
- firstParamPair
the first param pair, overwrite embedded params
- otherParamPairs
other param pairs, overwrite embedded params
- returns
transformed dataset
- Definition Classes
- Transformer
- Annotations
- @Since( "2.0.0" ) @varargs()
-
def
transformSchema(schema: StructType): StructType
Check transform validity and derive the output schema from the input schema.
Check transform validity and derive the output schema from the input schema.
We check validity for interactions between parameters during
transformSchema
and raise an exception if any parameter value is invalid. Parameter value checks which do not depend on other parameters are handled byParam.validate()
.Typical implementation should first conduct verification on schema change and parameter validity, including complex parameter interaction checks.
- Definition Classes
- KMeansModel → PipelineStage
- Annotations
- @Since( "1.5.0" )
-
val
uid: String
An immutable unique ID for the object and its derivatives.
An immutable unique ID for the object and its derivatives.
- Definition Classes
- KMeansModel → Identifiable
- Annotations
- @Since( "1.5.0" )
-
def
write: GeneralMLWriter
Returns a org.apache.spark.ml.util.GeneralMLWriter instance for this ML instance.
Returns a org.apache.spark.ml.util.GeneralMLWriter instance for this ML instance.
For KMeansModel, this does NOT currently save the training summary. An option to save summary may be added in the future.
- Definition Classes
- KMeansModel → GeneralMLWritable → MLWritable
- Annotations
- @Since( "1.6.0" )
Parameter setters
-
def
setFeaturesCol(value: String): KMeansModel.this.type
- Annotations
- @Since( "2.0.0" )
-
def
setPredictionCol(value: String): KMeansModel.this.type
- Annotations
- @Since( "2.0.0" )
Parameter getters
-
final
def
getDistanceMeasure: String
- Definition Classes
- HasDistanceMeasure
-
final
def
getFeaturesCol: String
- Definition Classes
- HasFeaturesCol
-
def
getK: Int
- Definition Classes
- KMeansParams
- Annotations
- @Since( "1.5.0" )
-
final
def
getMaxIter: Int
- Definition Classes
- HasMaxIter
-
final
def
getPredictionCol: String
- Definition Classes
- HasPredictionCol
-
final
def
getSeed: Long
- Definition Classes
- HasSeed
-
final
def
getSolver: String
- Definition Classes
- HasSolver
-
final
def
getTol: Double
- Definition Classes
- HasTol
-
final
def
getWeightCol: String
- Definition Classes
- HasWeightCol
(expert-only) Parameters
A list of advanced, expert-only (hyper-)parameter keys this algorithm can take. Users can set and get the parameter values through setters and getters, respectively.
-
final
val
initMode: Param[String]
Param for the initialization algorithm.
Param for the initialization algorithm. This can be either "random" to choose random points as initial cluster centers, or "k-means||" to use a parallel variant of k-means++ (Bahmani et al., Scalable K-Means++, VLDB 2012). Default: k-means||.
- Definition Classes
- KMeansParams
- Annotations
- @Since( "1.5.0" )
-
final
val
initSteps: IntParam
Param for the number of steps for the k-means|| initialization mode.
Param for the number of steps for the k-means|| initialization mode. This is an advanced setting -- the default of 2 is almost always enough. Must be > 0. Default: 2.
- Definition Classes
- KMeansParams
- Annotations
- @Since( "1.5.0" )
-
final
val
maxBlockSizeInMB: DoubleParam
Param for Maximum memory in MB for stacking input data into blocks.
Param for Maximum memory in MB for stacking input data into blocks. Data is stacked within partitions. If more than remaining data size in a partition then it is adjusted to the data size. Default 0.0 represents choosing optimal value, depends on specific algorithm. Must be >= 0..
- Definition Classes
- HasMaxBlockSizeInMB
-
final
val
solver: Param[String]
Param for the name of optimization method used in KMeans.
Param for the name of optimization method used in KMeans. Supported options:
- "auto": Automatically select the solver based on the input schema and sparsity: If input instances are arrays or input vectors are dense, set to "block". Else, set to "row".
- "row": input instances are processed row by row, and triangle-inequality is applied to accelerate the training.
- "block": input instances are stacked to blocks, and GEMM is applied to compute the distances. Default is "auto".
- Definition Classes
- KMeansParams → HasSolver
- Annotations
- @Since( "3.4.0" )
(expert-only) Parameter getters
-
def
getInitMode: String
- Definition Classes
- KMeansParams
- Annotations
- @Since( "1.5.0" )
-
def
getInitSteps: Int
- Definition Classes
- KMeansParams
- Annotations
- @Since( "1.5.0" )
-
final
def
getMaxBlockSizeInMB: Double
- Definition Classes
- HasMaxBlockSizeInMB